Open Set Classification: The open set setting considers knowledge acquired through training set to be incomplete, thus new unknown classes can be encountered during testing.
Scheirer [1]} developed open set classifiers in a one-vs-rest setting to balance the performance and the risk of labeling a sample far from the known training examples (termed as open space risk). Follow up works [2]}, [3]} extended the open set framework to multi-class classifier setting with probabilistic models to account for the fading away classifier confidences in case of unknown classes.
